Conventional Weather Stations Research Articles

Trends in Precipitation and Air Temperature Extremes and Their Relationship with Sea Surface Temperature in the Brazilian Midwest

The Brazilian Midwest has significant spatiotemporal variability in terms of precipitation and air temperature, making it more vulnerable to the occurrence of extreme weather events. The objective of this study is to characterize the trend of extreme climatic events regarding precipitation and air temperature in the Brazilian Midwest, and to analyze their relationship with Pacific and Atlantic Sea Surface Temperature anomalies (SSTAs). We used daily precipitation and air temperature data measured at 24 conventional weather stations. Pacific and Atlantic SSTA data were obtained from the Climate Prediction Center. The frequency of hot extremes had increased, while that of cold extremes had decreased significantly, thus highlighting the consistent warming across the Brazilian Midwest. The precipitation extremes had greater variability than the temperature extremes. Precipitation intensity increased in Amazonia, with no change in annual precipitation volume. The precipitation extremes in the Brazilian Savanna, Pantanal, and the Atlantic Forest did not have a well-defined pattern but indicated a trend towards a decrease in days with intense precipitation events. In general, the Equatorial Pacific and Atlantic Ocean (TNAI and TSAI) SSTAs were negatively correlated with precipitation extreme indices and positively correlated with air temperature extreme indices in the Amazon. However, the North Atlantic SSTAs were positively correlated with precipitation and air temperature extreme indices in the Brazilian Savanna and Pantanal. In addition, the Pacific SSTAs were positively correlated with precipitation intensity in the Atlantic Forest. Thus, the variability of the trends of precipitation and air temperature extreme indices in the Brazilian Midwest was observed, and it was surmised that this measure was significantly related to Pacific and Atlantic SSTAs.

Observations from Personal Weather Stations—EUMETNET Interests and Experience

The number of people owning a private weather station (PWS) and sharing their meteorological measurements online is growing worldwide. This leads to an unprecedented high density of weather observations, which could help monitor and understand small-scale weather phenomena. However, good data quality cannot be assured and thorough quality control is crucial before the data can be utilized. Nevertheless, this type of data can potentially be used to supplement conventional weather station networks operated by National Meteorological & Hydrological Services (NMHS), since the demand for high-resolution meteorological applications is growing. This is why EUMETNET, a community of European NMHS, decided to enhance knowledge exchange about PWS between NMHSs. Within these efforts, we have collected information about the current interest in PWS across NMHSs and their experiences so far. In addition, this paper provides an overview about the data quality challenges of PWS data, the developed quality control (QC) approaches and openly available QC tools. Some NMHS experimented with PWS data, others have already incorporated PWS measurements into their operational workflows. The growing number of studies with promising results and the ongoing development of quality control procedures and software packages increases the interest in PWS data and their usage for specific applications.

Automating reference temperature measurements for crop water stress index calculations: A case study on grapevines

The use of temperature as an indicator of water stress is gaining attention in agriculture. However, the need for reference temperature measurements in the field (Twet and Tdry) for normalization purposes (calculation of the crop water stress index - CWSI) inhibits the practical implementation of this technique. Therefore, in this study, two new models namely the heat transfer (HT) model and the empirical (EMP) model, are presented and compared with the standard method of physical reference temperature measurements and leaf energy balance calculation as an exploratory case study on grapevines. The HT model is a novel method, based on physical heat transfer principles and uses only input data obtained from a conventional weather station to determine reference temperatures. The EMP model is a simple method based on wet- and dry-bulb temperatures, calculated from ambient temperature and relative humidity measurements. To develop and evaluate the new models, physical measurements of the reference temperatures were taken in a commercial vineyard cv. Cabernet Sauvignon under different levels of water stress at two times during the day on seven days over the growing season. These physical measurements were used to optimize unknown parameters in the new methods by using the particle swarm optimization procedure. Input data for the model was collected by a conventional weather station located nearby the experimental vineyard block. In the validation process, it was found that the HT model can accurately predict the reference temperatures to within 0.5 °C and 1.0 °C for Twet and Tdry, respectively, reacting to environmental conditions as expected. The EMP model, requiring even less meteorological information, can accurately predict the reference temperatures to within 0.8 °C and 1.4 °C for Twet and Tdry, respectively. These proposed methods can provide reference temperatures that do not require physical measurements which can make the use of CWSI more practical and easier to implement for determining plant water stress in vineyards.

Assessing the Impacts of Meteorological Factors on COVID-19 Pandemic Using Generalized Estimating Equations.

BackgroundMeteorological factors have been proven to affect pathogens; both the transmission routes and other intermediate. Many studies have worked on assessing how those meteorological factors would influence the transmissibility of COVID-19. In this study, we used generalized estimating equations to evaluate the impact of meteorological factors on Coronavirus disease 2019 (COVID-19) by using three outcome variables, which are transmissibility, incidence rate, and the number of reported cases.MethodsIn this study, the data on the daily number of new cases and deaths of COVID-19 in 30 provinces and cities nationwide were obtained from the provincial and municipal health committees, while the data from 682 conventional weather stations in the selected provinces and cities were obtained from the website of the China Meteorological Administration. We built a Susceptible-Exposed-Symptomatic-Asymptomatic-Recovered/Removed (SEIAR) model to fit the data, then we calculated the transmissibility of COVID-19 using an indicator of the effective reproduction number (Reff). To quantify the different impacts of meteorological factors on several outcome variables including transmissibility, incidence rate, and the number of reported cases of COVID-19, we collected panel data and used generalized estimating equations. We also explored whether there is a lag effect and the different times of meteorological factors on the three outcome variables.ResultsPrecipitation and wind speed had a negative effect on transmissibility, incidence rate, and the number of reported cases, while humidity had a positive effect on them. The higher the temperature, the lower the transmissibility. The temperature had a lag effect on the incidence rate, while the remaining five meteorological factors had immediate and lag effects on the incidence rate and the number of reported cases.ConclusionMeteorological factors had similar effects on incidence rate and number of reported cases, but different effects on transmissibility. Temperature, relative humidity, precipitation, sunshine hours, and wind speed had immediate and lag effects on transmissibility, but with different lag times. An increase in temperature may first cause a decrease in virus transmissibility and then lead to a decrease in incidence rate. Also, the mechanism of the role of meteorological factors in the process of transmissibility to incidence rate needs to be further explored.

Maize sowing dates in the hinterland region of Northeast Brazil

Maize in Alagoas State, Northeast of Brazil, is mostly cultivated under rainfed conditions, however there are few studies evaluating the best sowing dates. The aim of this work was to assess sowing dates for the maize crop in the municipality of Arapiraca, located in a typical hinterland (Agreste) region of Alagoas State, based on the Water Requirement Satisfaction Index (WRSI). The WRSI was computed by means of a simple soil water balance model and the reference evapotranspiration was determined by the standard Penman-Monteith-FAO56 method. Daily values of air temperature, relative humidity, wind speed, sunshine duration and precipitation were measured in a conventional weather station, in the period from 1973 to 2008. The sowing dates for maize in Arapiraca with the lowest risks of water deficit lies between June 3 and June 23. Sowing maize in the beginning of April appears to be a very strategic alternative since it offers very low water deficit risk at the flowering crop stage. However, this might require water supplement at first stages of the crop due to the high-water deficit risks.

Continuity of daily temperature time series in the transition from conventional to automated stations for the Colombian coffee network

AbstractThe transition from conventional weather stations (CWSs) to automated weather stations (AWSs) of the Colombian coffee network has required testing their performance and adjusting the temperature measurements to ensure the continuity of the historical CWS series. In this study, the mean (Tmean), minimum (Tmin), and maximum temperature (Tmax) measurements of CWS and AWS operating in parallel at 36 locations between 2014 and 2019 were compared, and the biases of the daily temperature differences (CWS − AWS), the agreement index (d), and the percentage of data within the allowed range (PR05) were calculated. The most consistent method for calculating Tmean and Tmax for CWS was selected for use on the AWS data. With the standard normal homogeneity test and with the metadata, we found that the series of temperature differences between CWS and AWS was not homogeneous, instrument failures and sensor changes being the main causes of the lack of homogeneity. The statistical analyses indicated that the AWS data need to be adjusted to be continuous with the CWS series. To correct the temperature bias, two approaches were applied: quantile mapping and the additive constant. The results suggest that the quantile mapping adjustments improve the average bias at all stations but do not necessarily bring the percentage to within ±0.5°C. In Tmin and Tmean, 12 AWSs can give continuity with the historical series of the CWS, and for the rest of the stations and variables, the series of the AWSs are independent of the CWSs.

Daily prediction modeling of forest fire ignition using meteorological drought indices in the Mexican highlands

We analyzed the behavior of forest fires for daily prediction purposes in one of the regions with the highest fire incidence in Mexico. The main objective was to build logistic regression models (LRMs) for daily prediction of forest fire ignition based on meteorological drought indices. We built 252 LRMs for seven types of vegetation cover of greater representativeness and interest for the study area. Three dynamic variables were considered to estimate daily dryness in combustible fuels based on the effective drought index and the standardized precipitation-evapotranspiration index. Additionally, two weather data sources were included in drought indices: conventional weather stations (CWS) and automatic weather stations (AWS). Prediction efficiency assessment for LRMs was done through the relative operating characteristic (ROC) and model precision efficiency (MPE). The results show that LRMs using data from CWS performed relatively better than those based on data from AWS, as the former data sources have higher spatial density and thus generate predictions with higher accuracy (ROC ≥ 0.700, MPE ≥ 0.934). For both data sources, the use of standardized precipitation-evapotranspiration index as a fuel dryness estimator is recommended, as it reflects an atmospheric moisture balance between precipitation and reference evapotranspiration (ROC ≥ 0.734, MPE = 1). Such predictive models can be used as inputs in early warning systems for forest fire prevention or mitigation.

Assessment of heat stress and cloudiness probabilities in post-flowering of spring wheat and canola in the Southern Cone of South America

Episodes of heat stress constrain crop production and will be aggravated in the near future according to short and medium-term climate scenarios. Global increase in cloudiness has also been observed, decreasing the incident solar radiation. This work was aimed to quantify the probability of occurrence of heat stress and cloudiness, alone or combined, during the typical post-flowering period of wheat and canola in the Southern Cone of South America. Extended climate series (last 3–5 decades with daily register) of 33 conventional weather stations from Argentina, Brazil, Chile and Uruguay (23 to 40°S) were analysed considering the period from September to December. Two different daily events of heat stress were determined: (i) maximum daily temperature above 30 °C (T > 30 °C) and (ii) 5 °C above the historical average maximum temperature of that day (T + 5 °C). A cloudiness event was defined in our work as incident solar radiation 50% lower than the historical average radiation of that day (R50%). The T > 30 °C event increased its probability of occurrence throughout the post-flowering phase, from September to December. By contrast, the risk of T + 5 °C event decreased slightly, just like for R50%, and the higher the latitude, the lower the probability of R50%. The T > 30 °C plus R50% combined stresses reached greater cumulated probabilities during post-flowering, compared to T + 5 °C plus R50%, being 42% vs 15% in northernmost locations, 26% vs. 19% in central (between 31 and 35°S) and 28% vs. 1% in southernmost locations, respectively. A curvilinear relationship emerged between the monthly probability of combined stresses and the number of days with stress per month. In summary, T > 30 °C was the most frequent thermal stress during post-flowering in wheat and canola. Both combined stresses had a noticeable risk of occurrence, but T > 30 °C plus R50% was the highest. Evidence of the recent past and current occurrence of heat stress individually, and its combination with cloudiness events during post-flowering of temperate crops, serves as a baseline for future climate scenarios in main cropped areas in the Southern Cone of South America.

Comparison of meteorological data, related to reference evapotranspiration, from conventional and automatic stations in the Sertão and Agreste regions of Pernambuco, Brazil

The objective was to perform a comparative study of the meteorological elements data that most cause changes in the reference Evapotranspiration (ETo, mm) and its own value, of automatic weather stations AWS and conventional weather stations CWS of the Sertão and Agreste regions of Pernambuco State. The ETo was calculated on a daily scale using the standard method proposed by the Food and Agriculture Organization (FAO), Penman-Monteith (FAO-56). The ETo information obtained from AWS data can be used to update the weather database of stations, since there is a good relationship between the ETo data obtained from CWS and AWS, statistically determined by the Willmott's concordance index (d > 0.7). The observed variations in the weather elements: air temperature, relative humidity, wind speed, and global solar radiation have not caused significant changes in the ETo calculation.

Degradation of the Brazilian Cerrado: Interactions with human disturbance and environmental variables

The Cerrado is considered the most biodiverse savanna in the world, as well as the second largest Brazilian Biome. In recent decades, primarily due to agricultural expansion and wildfires, this biome has been severely degraded, losing nearly 50% of its original vegetation. This study aims to evaluate the degradation dynamics of the Cerrado vegetation as a function of human activity and climate variability. Products related to vegetation index (MOD13Q1) and an imaging spectroradiometer with moderate resolution from 2001 to 2018 were used to calculate the vegetation degradation index (VDI), which considers the vegetation cover as a function of human actions (deforestation, forest fires, agriculture, and farming). Rain and air temperature data were obtained from the Conventional Weather Station of Januária-Minas Gerais. Fire foci data were calculated using the moderate resolution imaging spectroradiometer - MODIS MCD14DL product sensor (TERRA/AQUA). Specifically, this study focused on the degradation of the vegetation in the Pandeiros River EPA (environmental protection area) that has increased due to the variability of climatic conditions, forest fires, and agricultural activities. The dry season provided greater sensitivity for the evaluation of the vegetation degradation process of the Pandeiros River EPA. The negative effects observed from the variation of climatic conditions and anthropogenic actions on the vegetation dynamics of the Pandeiros River EPA demand greater attention from administrative bodies on strategies for the conservation, preservation, and management of natural resources of conservation areas.

Diseño de una estación meteorológica automática para registrar las variables solar y eólica

La investigación tuvo por objetivo diseñar una estación meteorológica automática para registrar las variables solar y eólica. Se desarrolló desde la metodología de tipo descriptiva documental con diseño no experimental, lo cual, permitió en una primera fase, analizar datos meteorológicos con la finalidad de diagnosticar la necesidad de contar con el diseño de una propuesta de una estación meteorológica. Se observa que la radiación en este mes se osciló entre los 5.000 y 7.000 Wh/(m2-día), con algunas excepciones, llegando incluso a sobrepasar los 8.000 Wh/(m2-día). Es de notar que hubo días con una bajísima radiación solar dadas las lluvias y fuerte nubosidad encontradas en este mes. La estación meteorológica automática diseñada, evidencia una gran ventaja frente a las estaciones meteorológicas convencionales; esta puede ser usada en zonas con poca accesibilidad o en ausencia de red eléctrica.

Anthropogenic and climatic influences in the swamp environment of the Pandeiros River basin, Minas Gerais-Brazil.

Several environmental impacts are resulting from the process of anthropization and climate variability that have caused degradation of biomes and humid environments. Thus, the objective of this study was to evaluate the effect of the anthropic process and the variation of climatic conditions on the dynamics of the marsh vegetation in the Pandeiros River preservation area in the north of Minas Gerais, Brazil. The Enhanced Vegetation Index (EVI) of product MOD13Q1 and the gross primary productivity (GPP) of product MOD17A2 of the Moderate Resolution Imaging Spectroradiometer (MODIS) were used for the period from 2001 to 2017 were used in this process. Rain and air temperature data were obtained from the Conventional Weather Station of Januária-MG. The time series were submitted to the nonparametric statistical test of Mann-Kendall. The process of anthropization of the swamp area in the environmental protection area of the Pandeiros River/MG (EPA) showed a pattern of expansion of vegetation cover associated with the reduction of the water table, a phenomenon resulting from the silting process and reduction of the incidence of rain in this region, which contributes to the reduction of ecosystem services. Thus, understanding the influence of climatic variables on the dynamics of vegetation in humid environments, such as the EPA swamp area on the Pandeiros River, is essential for the preservation and recovery of these ecosystems and for the implementation of public policies for preservation and conservation.

Climate projections of chill hours and implications for olive cultivation in Minas Gerais, Brazil

Abstract: The objective of this work was to determine the accumulation of chill hours and to define the areas with aptitude for olive (Olea europaea) cultivation in the state of Minas Gerais, Brazil, as well as to analyze the impacts of climate change projections on chilling-hour requirements and climatic zoning, in two radiative forcing scenarios. The trigonometric method was used to quantify the number of chill hours, considering base temperatures (Tb) of 7.0, 9.5, and 13°C (high, medium, and low chill, respectively), and was applied to present climate (1983-2012) and to two future climate (2041-2070 and 2071-2100) conditions. The present climate data were obtained from 47 conventional weather stations, and the future climate data were obtained from three Earth system models (IPSL-CM5A-LR, MRI-CGCM3, and MIROC5). Future projections point to a decrease in the suitable areas for olive crop cultivation, particularly under representative concentration pathway (RCP) 8.5 and for olive cultivars with a high-chilling requirement (Tb=7.0ºC). Of the olive cultivars requiring medium chill (Tb=9.5ºC), only 2.6% (RCP 4.5) and 1.6% (RCP 8.5) will be suitable in the extreme south and in higher altitude areas of Minas Gerais, while, of those requiring low chill (Tb=13ºC), 11.8% (RCP 4.5) and 6.7% (RCP 8.5) will be suitable. If the climate projections become true, the cultivation of olive crops will be viable in the southern region and in higher altitude areas of the state of Minas Gerais.

Models for Estimating Reference Evapotranspiration in Different Periods in Rio Verde, Goiás, Brazil

The water management in irrigated agriculture begins determining the need of water for the culture. Therefore, it was intended to evaluate the performance of the models of estimation reference of evapotranspiration (ETo) with regard to the method Penman-Monteith (PM), standard method, for Brazilian Cerrado Region (tropical grassland/savannah). The climate elements were obtained from the conventional weather station of Rio Verde from January/1972 to December/2016. It was compared the performance of the daily average ETo, during the dry, rainy and annual periods, by the PM method with regard to another 26 methods. Through the coefficient of determination, it was verified the methods of Turc (T) and Radiation-Temperature (RT) approached more to the PM, at any time of the year, being able to replace the standard method. The ETo average in the annual period was 3.8 mm day-1, for the dry period due to the smallest amount of solar radiation, the period submitted lower levels of ETo. The other models in which were used fewer amounts of climate data, they overestimated or underestimated the PM model by up to 57.9% and 60.7% respectively. With the management of water in agriculture, water availability can be increased in the hydric bodies, characterizing it as a tool for water management with the rational use of water resources.

Predictive Capability of a High-Resolution Hydrometeorological Forecasting Framework Coupling WRF Cycling 3DVAR and Continuum

Abstract The typical complex orography of the Mediterranean coastal areas support the formation of the so-called back-building mesoscale convective systems (MCS) producing torrential rainfall often resulting in flash floods. As these events are usually very small-scaled and localized, they are hardly predictable from a hydrometeorological standpoint, frequently causing a significant amount of fatalities and socioeconomic damage. Liguria, a northwestern Italian region, is characterized by small catchments with very short hydrological response time and is thus extremely prone to the impacts of back-building MCSs. Indeed, Liguria has been hit by three intense back-building MCSs between 2011 and 2014, causing a total death toll of 20 people and several hundred millions of euros of damages. Consequently, it is necessary to use hydrometeorological forecasting frameworks coupling the finescale numerical weather prediction (NWP) outputs with rainfall–runoff models to provide timely and accurate streamflow forecasts. Concerning the aforementioned back-building MCS episodes that recently occurred in Liguria, this work assesses the predictive capability of a hydrometeorological forecasting framework composed by a kilometer-scale cloud-resolving NWP model (WRF), including a 6-h cycling 3DVAR assimilation of radar reflectivity and conventional weather stations data, a rainfall downscaling model [Rainfall Filtered Autoregressive Model (RainFARM)], and a fully distributed hydrological model (Continuum). A rich portfolio of WRF 3DVAR direct and indirect reflectivity operators has been explored to drive the meteorological component of the proposed forecasting framework. The results confirm the importance of rapidly refreshing and data intensive 3DVAR for improving the quantitative precipitation forecast, and, subsequently, the flash flood prediction in cases of back-building MCS events.

Validação das estimativas de precipitação do satélite TRMM no Estado de Mato Grosso, Brasil

Data of the rainfall are largely used in many researches, one of the methods of obtaining these data is through conventional weather station (CWS), but weather stations do not include a large area of information. Therefore, the use of data measured by satellite have been an alternative to the absence of weather stations or even of missing data. In this study, we pretend to evaluate the data of rainfall from the product 3B43 of TRMM satellite together with the data of CWS in the state of Mato Grosso related to the period of 2003 to 2013. For this, we have used as a means of comparison of data coming from CWS and those measured by satellite, the following statistical indicators: Mean Absolute Error (MAE), Mean Squared Error (MSE), Spearman’s correlation coefficient (r. Spearman) and the concordance index of Willmott (d). Mostly, the data of measurements through satellite were satisfactory, mainly to the months of June, July and August. The results indicate that the use of data measured through satellite can be an alternative to weather station measurements.

A 17-year Record of Meteorological Observations Across the Gran Campo Nevado Ice Cap in Southern Patagonia, Chile, Related to Synoptic Weather Types and Climate Modes

The network of long-term meteorological observations in Southernmost Patagonia is still sparse but crucial to improve our understanding of climatic variability, in particular in the more elevated and partially glaciated Southernmost Andes. Here we present a unique 17-year meteorological record (2000-2016) of four automatic weather stations across the Gran Campo Nevado Ice Cap (53$^{\circ}$S) in the Southernmost Andes (Chile) and the conventional weather station Jorge Schythe of the Instituto de la Patagonia in Punta Arenas for comparison. We revisit the relationship between in-situ observations and large-scale climate models as well as mesoscale weather patterns. For this purpose, a 37-year record of ERA Interim Reanalysis data has been used to compute a weather type classification based on a hierarchical correlation-based leader algorithm. The orographic perturbation on the predominantly westerly airflow determines the hydroclimatic response across the mountain range, leading to significant west-east gradients of precipitation, air temperature and humidity. Annual precipitation sums heavily drop within only tens of kilometers from \textasciitilde 7500 mm\,a$^{-1}$ to less than 800 mm\,a$^{-1}$. The occurrence of high precipitation events of up to 620 mm in 5 days and wet spells of up to 61 consecutive days underscore the year-around wet conditions in the Southernmost Andes. Given the strong link between large-scale circulation and orographically controlled precipitation, the synoptic-scale weather conditions largely determine the precipitation and temperature variability on all time scales. Major synoptic weather types with distinct low-pressure cells in the Weddell Sea or Bellingshausen Sea, causing a prevailing southwesterly, northwesterly or westerly airflow, determine the weather conditions in Southernmost Patagonia during 68$\%$ of the year. At Gran Campo Nevado, more than 80$\%$ of extreme precipitation events occur during the persistence of these weather types. The evolution of the El Ni\~{n}o Southern Oscillation and Antarctic Oscillation impose intra- and inter-annual precipitation and temperature variations. Positive Antarctic Oscillation phases on average are linked to an intensified westerly airflow and warmer conditions in Southernmost Patagonia. Circulation patterns with high-pressure influence leading to colder and dryer conditions in Southernmost Patagonia are more frequent during negative Antarctic Oscillation phases.

Are estimates of wind characteristics based on measurements with Pitot tubes and GNSS receivers mounted on consumer-grade unmanned aerial vehicles applicable in meteorological studies?

The objective of this paper is to empirically show that estimates of wind speed and wind direction based on measurements carried out using the Pitot tubes and GNSS receivers, mounted on consumer-grade unmanned aerial vehicles (UAVs), may accurately approximate true wind parameters. The motivation for the study is that a growing number of commercial and scientific UAV operations may soon become a new source of data on wind speed and wind direction, with unprecedented spatial and temporal resolution. The feasibility study was carried out within an isolated mountain meadow of Polana Izerska located in the Izera Mountains (SW Poland) during an experiment which aimed to compare wind characteristics measured by several instruments: three UAVs (swinglet CAM, eBee, Maja) equipped with the Pitot tubes and GNSS receivers, wind speed and direction meters mounted at 2.5 and 10 m (mast), conventional weather station and vertical sodar. The three UAVs performed seven missions along spiral-like trajectories, most reaching 130 m above take-off location. The estimates of wind speed and wind direction were found to agree between UAVs. The time series of wind speed measured at 10 m were extrapolated to flight altitudes recorded at a given time so that a comparison was made feasible. It was found that the wind speed estimates provided by the UAVs on a basis of the Pitot tube/GNSS data are in agreement with measurements carried out using dedicated meteorological instruments. The discrepancies were recorded in the first and last phases of UAV flights.

3D Remote survey of a rock wall hosting a multi-sensor monitoring system in a test-site (Acuto, Italy)

A multi-sensor monitoring system was installed in an abandoned quarry at Acuto (Frosinone - Central Italy) to test multi-sensing and multi-parametric remote techniques for early warning incase of rock failures threating strategic infrastructures. The multisensory monitoring system consist in: i) a meteo-climatic system, including a conventional weather stations and an innovative TSA-BOXone; ii) a geotechnical system, including thermometer for the rock mass temperature, strain-gauges for micro-fractures of rock mass, extensometers on open joints for detecting stress-strain conditions; iii) a nanoseismic monitoring system was also temporary installed to detect low magnitude vibrations to be regarded as precursors of rock failures. To correctly design the multi-parametric monitoring system, the rock wall was scanned to identify the main joint sets.Three GPS monographs were preliminary obtained in order to spatially geocoding the rock wall. From November 2015 to May 2016 remote scanning surveys were carried out on the quarry face by two different approaches: i) topographic 3D survey by Leica Total Station; ii) optical survey by 3D photos technique analyzed by Structure from Motion technique. The topographic survey provided high definition geocoded point clouds. These outputs were compared with the ones obtained by the SFM technique on 3D photos to test their reliability. At this aim, a Gaussian bi-modal distribution of the surveyed distances was obtained from each measurement; the comparison among the so derived distributions demonstrates that the computed errors are negligible and the main differences result at the boundaries of the sampled 3D domain. This comparison encourages the use of the photography technology by SfM technique to obtain multi-temporal geocoded point clouds for change detection analyses to point out evidences of scar zones due to slope failures. Thisapproach guarantees a very quick and accurate practice with easy management hardware and low software costs.

Sistema De Monitoreo de Variables Medioambientales Usando Una Red de Sensores Inalámbricos y Plataformas De Internet De Las Cosas

En este trabajo se propone un sistema para la recolección de datos meteorológicos usando una Red de Sensores Inalámbricos (RSI), capaz de transmitir los datos en tiempo real. El sistema logra automatizar los procesos de obtención de datos de manera continua y a largo plazo, por medio de un módulo de abastecimiento de energía solar que permite autonomía para su funcionamiento. Para la viabilidad del diseño e implementación de prototipos se propone la construcción de dos sistemas basados en DigiMesh y Wi-Fi, los que se pueden aplicar a diferentes escenarios como zonas urbanas y rurales. Adicionalmente se evalúa la transmisión de información a plataformas de Internet de las Cosas (IoT), en donde se gestionará y visualizará los datos obtenidos por los nodos. Este sistema fue concebido como una alternativa de bajo costo comparado con estaciones meteorológicas convencionales que posean estas prestaciones y está basado en componentes de hardware y software libre. Finalmente, se realizó la validación de los resultados obtenidos mediante un análisis estadístico con los datos registrados en una estación meteorológica Davis Vantage Pro, obteniendo un error relativo promedio máximo de 4,93%.